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Customer Case Study

Labor Optimization with Noodles and Company

Always evaluating how to build a more efficient kitchen

Each time we interact with restaurant leaders and their management teams, we ask them what their biggest operational challenge is – and they always say labor. And with a topic so vast as labor, a number of factors can impact how you optimize it: 

  • Table configuration
  • Section sizes 
  • Menu engineering
  • Ergonomics
  • Turnover

But most businesses don’t actually have a labor problem – they have a sales and deployment problem. Most commonly, restaurant operations have labor driving their sales. We need to adjust that mindset to get sales driving labor in order to deliver success for restaurants. From our 20 years in the labor management industry, we’ve found the most important factors that impact that success are:

  • Peak/fringe staffing
  • Scheduling experience
  • Work demand vs labor used
  • Inaccurate forecasting

The forecast is the prerequisite for everything.

When we look at data, on days when managers get their expected result out of the forecast and the shape of the day, they perform better on team and guest sentiment, spend and sales.

So what should a fast casual chain labor model look like? Noodles & Company has cracked the code. 

Noodles & Company, a fast casual chain headquartered in Denver, has just shy of 400 company restaurants, with 77 franchise restaurants. Dave Lehn, Director of Project Management at Noodles, oversees the corporate-owned locations and is constantly looking for new ways to streamline operations. Often, that entails rolling out a new system or working with 3rd party vendors to implement new technology company-wide. 

Lehn said, “Many of us have had the standard labor model being purely linear. If you have X amount of sales you get X amount of hours. But the idea of having sales drive your labor, and not the other way around – we knew we had to approach this another way.” 

“We theorized and built out a labor model to save and reduce labor by about 1.5 hrs a day. Within just a few months of doing this in a subset of stores, we are realizing significant labor savings everyday.”

More prep, less time. 

An effective labor modeling process will remove the degree of feel, perception, habit, and historic accepted norms and rely more on data and metrics that have been built to reflect what is actually required, allowing management to obtain their intended result.

Noodles, like many other restaurants, used to do food prep once a day, but found it to be time consuming for opening team members. Lehn said, “One of the things we’ve discovered was the fair amount of idle time in the middle of the day that we could take advantage of. So we moved some of the prep over there. In doing so, we theorized and built out a labor model to save and reduce labor by about 1.5 hrs a day. Within just a few months of doing this in a subset of stores, we are realizing significant labor savings everyday.” 

Many operators approach labor optimization by first attempting to cut labor spend. However, the concept of ‘cutting labor’ should be repositioned as “use the labor you need”, as the technology does not replace the need for great people. The point is to enhance their ability to make great decisions so that managers can make realistic plans which lead to deliverable commitments to team members and other stakeholders.

Sometimes less is more. 

The fast-casual chain didn’t stop there. Upon looking at gaining greater efficiencies, the Noodles closing staff had the greatest opportunity. Lehn said, “HotSchedules provided the insight for us to see more could be done. And of course, operationally we had to make sure it made sense.” 

What they found was that having 5-6 people performing closing shift duties, it actually took longer to close than if they had only 4. Lehn said, “We worked with our most seasoned operators and discovered that in many cases, with 4 people, we were closing the restaurants in 45 minutes. With 5 folks, it was taking 1 full hour!”

Noodles continued the national roll out of the HotSchedules labor optimization solution in the following months and has had great success. 

Lehn concluded, “Before we rolled this out, we always like to start with why. We don’t want to cut labor in half. We want to know how we can be smarter and apply it when we need it. Managers should not have to be data scientists. They should focus on interactions with team members and better serving our guests – and deploy labor around that. HotSchedules helps us do this successfully.”

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